| from __future__ import annotations | |
| from typing import Any | |
| from datasets import Dataset | |
| from mteb.encoder_interface import Encoder, EncoderWithQueryCorpusEncode | |
| from mteb.MTEBResults import ScoresDict | |
| from ..evaluation.evaluators import RerankingEvaluator | |
| from .AbsTask import AbsTask | |
| class AbsTaskReranking(AbsTask): | |
| """Abstract class for re-ranking experiments. | |
| self.load_data() must generate a huggingface dataset with a split matching self.metadata_dict["eval_splits"], and assign it to self.dataset. It must contain the following columns: | |
| query: str | |
| positive: list[str] | |
| negative: list[str] | |
| """ | |
| def __init__(self, **kwargs): | |
| super().__init__(**kwargs) | |
| def _evaluate_subset( | |
| self, | |
| model: Encoder | EncoderWithQueryCorpusEncode, | |
| data_split: Dataset, | |
| **kwargs: Any, | |
| ) -> ScoresDict: | |
| evaluator = RerankingEvaluator(data_split, **kwargs) | |
| scores = evaluator(model) | |
| self._add_main_score(scores) | |
| return scores | |
| def _add_main_score(self, scores: ScoresDict) -> None: | |
| scores["main_score"] = scores[self.metadata.main_score] | |